OMI/Aura Nitrogen Dioxide Standard Product with Improved Surface and Cloud Treatments

Abstract

Abstract. We present a new and improved version (V4.0) of the NASA standard nitrogen dioxide (NO2) product from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This version incorporates the most salient improvements for regional OMI NO2 products suggested by expert users, and enhances the NO2 data quality in several ways on a global scale through improvements to the air mass factors (AMFs) used in the retrieval algorithm. The algorithm is based on a conceptually new, geometry-dependent surface Lambertian equivalent reflectivity (GLER) operational product that is available on an OMI pixel basis. GLER is calculated using the vector linearized discrete ordinate radiative transfer (VLIDORT) model, which uses as input high resolution bidirectional reflectance distribution function (BRDF) information from NASA’s Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) instruments over land and the wind-dependent Cox–Munk wave-facet slope distribution over water, the latter with contribution from the water-leaving radiance. The GLER combined with consistently retrieved oxygen dimer (O2-O2) absorption-based cloud fractions and pressures provide high-quality data inputs to the new NO2 AMF scheme. The new AMFs increase the retrieved tropospheric NO2 by up to 50 % in highly polluted areas; these differences arise from both cloud and surface BRDF effects as well as biases between the new MODIS-based and previously used OMI-based climatological surface reflectance data sets. We quantitatively evaluate the new NO2 product using independent observations from ground-based and airborne instruments. The improved NO2 data record can be used for studies related to emissions and trends of nitrogen oxides (NOx) and co-emitted gases. The new V4.0 data and relevant explanatory documentation are publicly available from the NASA Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets/OMNO2_V003/summary/), and we encourage their use over previous versions of OMI NO2 products.

Surface and satellite observations of air pollution in India during COVID-19 lockdown: Implication to air quality

“…With a 2600 km swath, it provides daily global coverage. For this study, we have used the latest version (V4.0) of the NASA standard tropospheric NO 2 column density Level-2 (L2) product available from the Goddard Earth Sciences Data and Information Services Center (GES DISC) and Level-3 (L3) gridded product available from the Aura Validation Data Center (AVDC) website ( Lamsal et al, 2020 ). The ground resolution of the L2 product varies across swath ranging from 13 km x 24 km (along-track x across-track) at swath center to 24 km x 160 km at the outermost edges of the swath.…”

TROPOMI NO2 in the United States: A Detailed Look at the Annual Averages, Weekly Cycles, Effects of Temperature, and Correlation With Surface NO2 Concentrations

“…The AMF is the most uncertain quantity in the retrieval algorithm (Lorente, Folkert Boersma, et al, 2017) and is a function of the surface reflectance, the NO 2 vertical profile, and scattering in the atmosphere among other factors. Using accurate and high-resolution data (spatially and temporally) as inputs in calculating the AMF can significantly reduce the overall errors of the AMF (S. Choi et al, 2019;Goldberg et al, 2017;Lamsal, 2020;Laughner, Zare, & Cohen, 2016, Laughner, Zhu, & Cohen, 2019Lin et al, 2015;M. Liu et al, 2019;Russell, Perring, et al, 2011;Zhao et al, 2020) and thus the tropospheric vertical column content.…”

Comment on acp-2020-1291

“…These times are optimal for assessing local emission conditions since they are related to the highest NOx levels typically produced as a result of both commuter traffic peaks 205 and the shallow morning planetary boundary layer (Tong et al 2015). Satellite-based temporal trends are calculated from the monthly NO2 product retrieved from the Ozone Monitoring Instrument (OMI) aboard the Aura satellite (Lamsal et al, 2020). A weighting function is introduced to combine the surface-based and satellite-based temporal trends to acquire the merged projection adjustment factor (AF) for a specified region:…”